Strategic advice & alerts to leverage data analytics & new technologies
Leverage data and the technologies that generate it, from IoT to AI/machine learning, wearables, blockchain, and more, to improve decision-making, enrich collaboration and enable new services.
Theresa Jefferson and Gloria Phillips-Wren discuss modeling for disaster response. This is vital for practitioner/manager decision making to reduce the impact of a natural or man-made disaster. The authors examine the concept of technology embeddedness, noting that emergency managers must trust the technology and show a preference to use it prior to an actual disaster; the time to integrate technology into disaster recovery operations is not during a disaster. They explain how to effectively appropriate, integrate, and use modeling technologies for disaster response and, therefore, recovery.
Frederic Adam and Paidi O’Raghallaigh tackle the current healthcare crisis. They shine a light on the opportunities provided by medical decision support for clinicians and patients and identify a number of challenges to achieving connected health, which they define as “the use of technology-based solutions to deliver healthcare services remotely.” The article proposes a connected health blueprint that may well pave the way for future connected health systems.
The challenge for this issue was to accurately represent the diversity of research in the DSS arena while also giving a glimpse of the cutting-edge DSSs of tomorrow.
Tom Butler and Leona O’Brien provide a timely perspective on AI in the financial industry. Their article provides a pragmatic perspective on the capabilities of AI and pours cold water on some of the hyperbolic claims made about AI and ML in the fintech and regtech space. The authors suggest a direction and guidelines for future research for AI to realize its potential in the financial services sector.
Ciara Heavin and Daniel Power provide an overview of the design and development of modern BI and data-driven DSSs. They identify challenges and opportunities for managers and provide a sociotechnical view of DSSs by demonstrating practical guidelines for the people, process, and technology components of modern BI and data-driven DSSs.
To meet increasingly elevated customer expectations, organizations are implementing detailed strategies for distributing customer experience (CX) practices across the organization. This includes defining and standardizing the “customer journey” across various channels in order to strengthen their brand, increase customer loyalty, reduce costs, make better use of customer feedback, and so on. Organizations are also investing in leading technologies designed to enhance CX, regardless of which channels customers choose to engage with them.
Businesses are implementing analytics and trying to use data to uncover new insights about their operations, customers, suppliers, employees, and so on. Even though the idea of using analytics is exciting, these types of projects are not for the faint-hearted — at least if you’re trying to implement analytics across the entire enterprise.
Identifying and developing new drugs and conducting clinical trials involve complex and lengthy (i.e., costly) processes that require researchers and drug manufacturers to integrate, manage, and analyze incredible amounts of data while at the same time collaborate with other medical research and pharma companies in their efforts. Pharmaceutical and biotechnology companies are using artificial intelligence (AI) to optimize the discovery and evaluation of new drug compounds, to explore patient and efficacy data, and to develop and bring new therapies to market.